{"id":1045,"date":"2026-04-17T07:42:00","date_gmt":"2026-04-17T07:42:00","guid":{"rendered":"https:\/\/www.hostrunway.com\/blog\/?p=1045"},"modified":"2026-03-26T07:08:25","modified_gmt":"2026-03-26T07:08:25","slug":"nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026","status":"publish","type":"post","link":"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/","title":{"rendered":"NVIDIA B200 vs AMD MI325X: Which Is the Real King of AI Inference in 2026?"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_Great_Inference_Pivot_of_2026\" >The Great Inference Pivot of 2026<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Architectural_Deep_Dive_Blackwell_vs_CDNA_3\" >Architectural Deep Dive: Blackwell vs. CDNA 3<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#NVIDIA_Blackwell_B200\" >NVIDIA Blackwell B200<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#AMD_Instinct_MI325X\" >AMD Instinct MI325X<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Interconnects_How_Multi-GPU_Systems_Talk\" >Interconnects: How Multi-GPU Systems Talk<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_VRAM_War_Why_256GB_Changes_the_Math\" >The VRAM War: Why 256GB Changes the Math<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#AMDs_Lead_in_Memory\" >AMD&#8217;s Lead in Memory<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_%E2%80%9CSingle-Node%E2%80%9D_Advantage\" >The &#8220;Single-Node&#8221; Advantage<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Performance_Benchmarks_Throughput_vs_Latency\" >Performance Benchmarks: Throughput vs. Latency<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Raw_Throughput\" >Raw Throughput<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Time_to_First_Token_TTFT\" >Time to First Token (TTFT)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#NVIDIA_B200_vs_AMD_MI325X_Benchmarks_Long-Context_RAG\" >NVIDIA B200 vs AMD MI325X Benchmarks: Long-Context RAG<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Software_Ecosystem_Is_ROCm_Finally_Ready_for_CUDA\" >Software Ecosystem: Is ROCm Finally Ready for CUDA?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#CUDAs_Maturity\" >CUDA&#8217;s Maturity<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#ROCm_6x_Progress\" >ROCm 6.x Progress<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_Triton_Factor\" >The Triton Factor<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_Economics_of_Token_Generation_Cost-per-Dollar_Analysis\" >The Economics of Token Generation: Cost-per-Dollar Analysis<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Purchase_Price\" >Purchase Price<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Cost-per-Token\" >Cost-per-Token<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Power_Efficiency_Tokens_per_Watt\" >Power Efficiency: Tokens per Watt<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#AMD_vs_NVIDIA_for_LLM_inference_2026_The_Business_Case_Summary\" >AMD vs NVIDIA for LLM inference 2026: The Business Case Summary<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Use_Case_Analysis_When_to_Choose_Which\" >Use Case Analysis: When to Choose Which?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Choose_the_NVIDIA_B200_If_You\" >Choose the NVIDIA B200 If You:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Choose_the_AMD_MI325X_If_You\" >Choose the AMD MI325X If You:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#MI325X_vs_H200_Inference_Speed_A_Quick_Note\" >MI325X vs H200 Inference Speed: A Quick Note<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#The_2026_Roadmap_Blackwell_Ultra_and_the_Coming_Rubin_Era\" >The 2026 Roadmap: Blackwell Ultra and the Coming Rubin Era<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#FAQs\" >FAQs<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q1_Which_GPU_offers_the_most_competitive_cost-per-token_for_high-density_inference_workloads_in_2026\" >Q1: Which GPU offers the most competitive cost-per-token for high-density inference workloads in 2026?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q2_Does_the_256GB_HBM3e_memory_on_the_AMD_MI325X_allow_larger_models_to_be_deployed_on_single-node_configurations_compared_to_the_NVIDIA_B200\" >Q2: Does the 256GB HBM3e memory on the AMD MI325X allow larger models to be deployed on single-node configurations compared to the NVIDIA B200?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q3_How_does_the_8_TBs_memory_bandwidth_of_the_NVIDIA_Blackwell_B200_impact_performance_for_latency-critical_real-time_AI_applications\" >Q3: How does the 8 TB\/s memory bandwidth of the NVIDIA Blackwell B200 impact performance for latency-critical, real-time AI applications?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q4_For_RAG_pipelines_requiring_massive_KV_caches_does_the_AMD_MI325X_provide_a_significant_throughput_advantage_over_NVIDIA_counterparts\" >Q4: For RAG pipelines requiring massive KV caches, does the AMD MI325X provide a significant throughput advantage over NVIDIA counterparts?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q5_Is_ROCm_mature_enough_in_2026_to_replace_CUDA_for_production_LLM_inference\" >Q5: Is ROCm mature enough in 2026 to replace CUDA for production LLM inference?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.hostrunway.com\/blog\/nvidia-b200-vs-amd-mi325x-which-is-the-real-king-of-ai-inference-in-2026\/#Q6_Should_a_startup_choose_the_MI325X_or_the_B200_for_its_first_GPU_cluster\" >Q6: Should a startup choose the MI325X or the B200 for its first GPU cluster?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"The_Great_Inference_Pivot_of_2026\"><\/span><strong>The Great Inference Pivot of 2026<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The AI world has shifted. Training huge models was the topic of conversation a couple of years ago. Running them is the real money in 2026.<\/p>\n\n\n\n<p>The cost of production inference is currently exceeding training expenses in most AI firms. Each token used by a user is compute expensive. Any second of delay is costly to the customer. The issue about the appropriate GPU is no longer a scholarly topic. It is a decision that is vital to the business.<\/p>\n\n\n\n<p>That is where the NVIDIA <a href=\"https:\/\/www.hostrunway.com\/gpu-server\/nvidia-b200.php\" title=\"\">B200<\/a> vs AMD MI325X debate comes in.<\/p>\n\n\n\n<p>There is the Blackwell B200 of NVIDIA on the one hand. It is the undisputed performance leader. It has some of the best throughput figures. Across the street, there is AMD Instinct MI325X. It has a memory capacity of 256GB, the highest in this category of <a href=\"https:\/\/www.hostrunway.com\/powerful-gpus.php\" title=\"\">GPUs<\/a>. It is the memory challenger with high memory forcing its way into the territory that has long been dominated by NVIDIA.<\/p>\n\n\n\n<p>The story of 2026 is less complicated: is raw compute power (advantage NVIDIA) or aggregate memory (advantage AMD) more relevant to the current, long context AI tasks?<\/p>\n\n\n\n<p>This article has disaggregated it, in sections, such that you leave with your exact answer in relation to your needs.<\/p>\n\n\n\n<p>Also Read : <a href=\"https:\/\/www.hostrunway.com\/blog\/how-to-choose-the-right-gpu-for-your-ai-project-in-2026-a-complete-guide\/\" title=\"\">How to Choose the Right GPU for Your AI Project in 2026 \u2013 A Complete Guide<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"Architectural_Deep_Dive_Blackwell_vs_CDNA_3\"><\/span>Architectural Deep Dive: Blackwell vs. CDNA 3<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In order to make an intelligent selection, you must know what is contained in every chip. The plain-English version is found here.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"NVIDIA_Blackwell_B200\"><\/span>NVIDIA Blackwell B200<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Process node: 4nm<\/li>\n\n\n\n<li>Transistors: 208 billion<\/li>\n\n\n\n<li>Precision: FP4 Precision Engine, Second Generation Transformer Engine is now optimized<\/li>\n<\/ul>\n\n\n\n<p>The Transformer Engine selects the most suitable number format of every layer within a neural network automatically. FP4 support implies that there are fewer computations per second with a reduced memory overhead.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"AMD_Instinct_MI325X\"><\/span>AMD Instinct MI325X<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Architecture: CDNA 3<\/li>\n\n\n\n<li>Design: Chiplet-based, meaning multiple smaller dies are connected on a single package.<\/li>\n<\/ul>\n\n\n\n<p>This chiplet design is the secret weapon of AMD in the area of memory density. AMD does not use a single large die, but smaller highly specialized chips stacked together. This gives them the ability to stuff a lot more memory.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Interconnects_How_Multi-GPU_Systems_Talk\"><\/span>Interconnects: How Multi-GPU Systems Talk<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Scaling to more than a single GPU, the speed with which the chips are communicating becomes important.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Feature<\/th><th>NVIDIA B200<\/th><th>AMD MI325X<\/th><\/tr><\/thead><tbody><tr><td>Interconnect<\/td><td>NVLink 5<\/td><td>Infinity Fabric<\/td><\/tr><tr><td>Bandwidth<\/td><td>1.8 TB\/s<\/td><td>896 GB\/s<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>NVLink 5, which is offered by NVIDIA at 1.8 TB\/s, is much faster when it comes to data sharing between multiple GPUs. This is actually beneficial to workloads that require multiple GPUs to be tightly coordinated. The Infinity Fabric used by AMD remains sound in numerous applications, but it cannot compete with NVLink 5 when it comes to full bandwidth.<\/p>\n\n\n\n<p>Also Read : <a href=\"https:\/\/www.hostrunway.com\/blog\/h200-vs-b200-vs-mi300x-comparison-which-gpu-is-best-for-llm-training\/\" title=\"\">H200 vs B200 vs MI300X Comparison: Which GPU is Best for LLM Training<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"The_VRAM_War_Why_256GB_Changes_the_Math\"><\/span>The VRAM War: Why 256GB Changes the Math<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>It is the largest card of AMD to play, and it is a significant one.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"AMDs_Lead_in_Memory\"><\/span>AMD&#8217;s Lead in Memory<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AMD MI325X: 256GB of HBM3e memory<\/li>\n\n\n\n<li>NVIDIA B200: 192GB of HBM3e memory<\/li>\n<\/ul>\n\n\n\n<p>This 64GB difference is not just a sheet figure. It alters what can be performed physically at a single node of the server.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"The_%E2%80%9CSingle-Node%E2%80%9D_Advantage\"><\/span>The &#8220;Single-Node&#8221; Advantage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The current big language models (LLMs) such as Llama 4 Scout have very massive weights when embedded into memory. Having 256GB, a developer on the MI325X can fit larger models completely on a single GPU. At 192GB on the B200, one model may need two GPUs in parallel.<\/p>\n\n\n\n<p>Use of two GPUs as opposed to one implies:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Extra complications in configuration and coordination.<\/li>\n\n\n\n<li>Higher hardware costs<\/li>\n\n\n\n<li>More points of failure<\/li>\n<\/ul>\n\n\n\n<p>This is important to startups and AI teams that are resource constrained. The fact that one can run a 100B+ parameter model with one node compared to two is a real simplification of operation.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" style=\"font-size:20px\">Memory Bandwidth: Speed vs. Capacity<\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Metric<\/th><th>NVIDIA B200<\/th><th>AMD MI325X<\/th><\/tr><\/thead><tbody><tr><td>Memory<\/td><td>192GB HBM3e<\/td><td>256GB HBM3e<\/td><\/tr><tr><td>Bandwidth<\/td><td>8 TB\/s<\/td><td>6 TB\/s<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The bandwidth of NVIDIA is much higher at 8 TB\/s. Imagine it has a pipe which is narrower and transfers water at a greater pressure. AMD has a lower speed per second of 6 TB\/s, but the total water of its bucket is larger.<\/p>\n\n\n\n<p>NVIDIA speed difference can be seen clearly in applications that are sensitive to latency. AMD has much bigger capacity, which is an advantage in the applications that require to store large models or huge context windows in memory.<\/p>\n\n\n\n<p>Also Read : <a href=\"https:\/\/www.hostrunway.com\/blog\/h100-vs-b200-vs-gb200-which-gpu-should-you-rent-right-now-for-ai-in-2026\/\" title=\"\">H100 vs B200 vs GB200: Which GPU Should You Rent Right Now for AI in 2026?<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Performance_Benchmarks_Throughput_vs_Latency\"><\/span>Performance Benchmarks: Throughput vs. Latency<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Real-world performance depends on your workload. Here is where each chip leads.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Raw_Throughput\"><\/span>Raw Throughput<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>On standard, short context queries, the B200 beats in tokens-per-second. It has a definite advantage since it is able to handle a large volume of short requests simultaneously because of its greater compute density and precision in FP4. When you are operating a high-volume chatbot with millions of users and short conversations, the B200 provides a higher amount of output per second.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Time_to_First_Token_TTFT\"><\/span>Time to First Token (TTFT)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This measure is the duration that it takes a user before viewing the first word of a response. This is the responsiveness which users experience in real-time applications.<\/p>\n\n\n\n<p>NVIDIA typically wins here. The raw compute power of the B200 is such that it begins to produce output at a higher rate. This is a big deal with real-time AI assistants, chatbots used with customers and trading tools that require low latency.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"NVIDIA_B200_vs_AMD_MI325X_Benchmarks_Long-Context_RAG\"><\/span>NVIDIA B200 vs AMD MI325X Benchmarks: Long-Context RAG<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It is here that AMD turns the tables.<\/p>\n\n\n\n<p>Retrieval-Augmented Generation (RAG) pipelines draw in the great fragments of foreign data prior to creation of a reply. The data is stored in the KV (Key-Value) cache of the GPU when it is generated. The bigger your KV cache the bigger your model views.<\/p>\n\n\n\n<p>The MI325X has 256GB of memory, making it much larger than the B200 in terms of caches of KV. With long-document summarization and RAG workloads, AMD gives a higher throughput per node in NVIDIA B200 vs AMD MI325X benchmarks with 128k+ token windows.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Workload<\/th><th>Winner<\/th><\/tr><\/thead><tbody><tr><td>Short-context chatbot (throughput)<\/td><td>NVIDIA B200<\/td><\/tr><tr><td>Real-time TTFT \/ low latency<\/td><td>NVIDIA B200<\/td><\/tr><tr><td>Long-context RAG (128k+ tokens)<\/td><td>AMD MI325X<\/td><\/tr><tr><td>Single-node large model hosting<\/td><td>AMD MI325X<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Also Read : <a href=\"https:\/\/www.hostrunway.com\/blog\/gpu-dedicated-server-vs-cloud-which-is-best-for-your-ai-and-compute-needs-in-2026\/\" title=\"\">GPU Dedicated Server vs Cloud: Which is Best for Your AI and Compute Needs in 2026?<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"Software_Ecosystem_Is_ROCm_Finally_Ready_for_CUDA\"><\/span>Software Ecosystem: Is ROCm Finally Ready for CUDA?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The software gap is the largest invisible asset that NVIDIA had in the years. That being said, we would like to consider the situation in 2026.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"CUDAs_Maturity\"><\/span>CUDA&#8217;s Maturity<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The CUDA platform created by NVIDIA has been developed in more than 15 years. It has an enormous library support, optimization platform and developer community. Switching would have some real learning curve in case your team is already operating on CUDA-based frameworks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"ROCm_6x_Progress\"><\/span>ROCm 6.x Progress<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The open-source ROCm stack of AMD has come in earnest. In 2026, ROCm 6.x provides the so-called production parity of large frameworks that many teams have now dubbed as such:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>PyTorch: Full support, including optimized kernels<\/li>\n\n\n\n<li>JAX: Stable and production-ready on ROCm<\/li>\n\n\n\n<li>vLLM: One of the most popular LLM serving frameworks, now well-supported on AMD hardware<\/li>\n<\/ul>\n\n\n\n<p>Inference pipeline teams do not have to rewrite their code to switch to AMD anymore. That was the blocker in 2023 and 2024. It is much less now.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"The_Triton_Factor\"><\/span>The Triton Factor<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The Triton programming language of OpenAI is creating hardware choice as a less locked-in choice. Triton assembles effective GPU kernels which are compatible with both NVIDIA and AMD-based hardware.<\/p>\n\n\n\n<p>This is significant. When your code is written in the inference kernel Triton, it also runs successfully on a B200 or MI325X. The chip beneath it becomes less significant to your software stack.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"The_Economics_of_Token_Generation_Cost-per-Dollar_Analysis\"><\/span>The Economics of Token Generation: Cost-per-Dollar Analysis<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This is where the AMD Instinct MI325X vs Blackwell B200 comparison gets very interesting for budget-conscious teams.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Purchase_Price\"><\/span>Purchase Price<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>GPU<\/th><th>Estimated Price<\/th><\/tr><\/thead><tbody><tr><td>NVIDIA B200<\/td><td>$35,000 to $40,000 per card<\/td><\/tr><tr><td>AMD MI325X<\/td><td>$16,000 to $20,000 per card<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The MI325X is cheaper by a factor of about half the price. Such a difference is transformative to a startup or SME setting up a <a href=\"https:\/\/www.hostrunway.com\/gpu-dedicated-server.php\" title=\"\">GPU<\/a> cluster. Ten MI325X cards are about the price of five B200 cards. It can have more total memory capacity and have more nodes at the same expense.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Cost-per-Token\"><\/span>Cost-per-Token<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Specific inference workload independent benchmarks indicate that AMD can provide 30 to 40 percent better number of tokens per dollar that NVIDIA provides in specific settings. This is not universal. The gap decreases in the case of short-context, high-throughput tasks. However, in long context inference, which the AMD memory is so good at, the cost advantage is tangible.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Power_Efficiency_Tokens_per_Watt\"><\/span>Power Efficiency: Tokens per Watt<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The two chips attract about 1kW when full loaded. That is a significant point of departure. One of the major continued costs in any data center is power costs.<\/p>\n\n\n\n<p>The number of tokens per watt is now a sustainability KPI of the major AI companies in 2026. The B200 performs better than the MI325X on standard workloads with respect to its production of tokens per watt on raw compute efficiency. The MI325X is more competitive on this measure when used on long-context workloads where AMD does not suffer the cost of multi-GPU systems.<\/p>\n\n\n\n<p>This calculation is of interest to the fintech companies, SaaS companies, and gaming platforms that observe their energy bills.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"AMD_vs_NVIDIA_for_LLM_inference_2026_The_Business_Case_Summary\"><\/span>AMD vs NVIDIA for LLM inference 2026: The Business Case Summary<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Factor<\/th><th>NVIDIA B200<\/th><th>AMD MI325X<\/th><\/tr><\/thead><tbody><tr><td>Card price<\/td><td>$35k to $40k<\/td><td>$16k to $20k<\/td><\/tr><tr><td>Memory<\/td><td>192GB<\/td><td>256GB<\/td><\/tr><tr><td>Tokens\/dollar (long-context)<\/td><td>Baseline<\/td><td>30 to 40% better<\/td><\/tr><tr><td>Single-node large model support<\/td><td>Needs 2 GPUs<\/td><td>Fits on 1 GPU<\/td><\/tr><tr><td>Software maturity<\/td><td>Higher<\/td><td>Growing fast<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"Use_Case_Analysis_When_to_Choose_Which\"><\/span>Use Case Analysis: When to Choose Which?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The right chip is dependent on workload. This is an effective decision guide.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Choose_the_NVIDIA_B200_If_You\"><\/span>Choose the NVIDIA B200 If You:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Are training or fine-tuning frontier models?<\/li>\n\n\n\n<li>Require less than 100ms turnaround on real-time customer-facing applications.<\/li>\n\n\n\n<li>Already have well-integrated environments based on proprietary stack with NVIDIA (CUDA libraries, TensorRT, NeMo).<\/li>\n\n\n\n<li>Scale on a grandiose level with the ecosystem maturity of NVIDIA minimizing engineering risk.<\/li>\n<\/ul>\n\n\n\n<p>Best fit: Large enterprise AI labs, real-time trading platforms, production chatbots with initiated hard latency SLAs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"Choose_the_AMD_MI325X_If_You\"><\/span>Choose the AMD MI325X If You:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Provide high-density inference APIs, in which cost-per-token leads to profitability.<\/li>\n\n\n\n<li>Run long context window applications (128k+ tokens) like RAG, document analysis, or code review.<\/li>\n\n\n\n<li>Requirement to deploy huge models in a single node to minimize the complexity of infrastructure.<\/li>\n\n\n\n<li>Are a startup or SME that aims to optimize the runway on the number of dollars spent on computers?<\/li>\n<\/ul>\n\n\n\n<p>Best fit: AI startups, LLM API providers, variable demand SaaS, ML teams which grow inference without exhausting the budget.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:20px\"><span class=\"ez-toc-section\" id=\"MI325X_vs_H200_Inference_Speed_A_Quick_Note\"><\/span>MI325X vs H200 Inference Speed: A Quick Note<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>NVIDIA H200 clusters are still in use in many teams. Compared to MI325X vs H200 inference speed on long context workloads, the MI325X is very competitive, and costs much less. In case you are deciding between new inference capacity between <a href=\"https:\/\/www.hostrunway.com\/gpu-server\/nvidia-h200.php\" title=\"\">H200<\/a> and MI325X, then the case for AMD is quite strong.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"The_2026_Roadmap_Blackwell_Ultra_and_the_Coming_Rubin_Era\"><\/span>The 2026 Roadmap: Blackwell Ultra and the Coming Rubin Era<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The process of purchasing GPU infrastructure is a long-term investment. Here is what is coming.<\/p>\n\n\n\n<p><strong>NVIDIA: Blackwell Ultra (B300) and Vera Rubin<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Blackwell Ultra (B300): NVIDIA is gaining momentum. The memory size of the B300 is going to be 288GB, which bridges the gap with the present 256GB of AMD. Expected in late 2026.<\/li>\n\n\n\n<li>Vera Rubin Architecture: The next significant architecture at NVIDIA following Blackwell is prospective to cut the token costs by 10x. This is at a tender age but it is an indication of where the market is going.<\/li>\n<\/ul>\n\n\n\n<p>When you buy B200 hardware today, then be aware that NVIDIA is aggressive in its roadmap. The gap in memory with AMD will be reduced towards the end of the year.<\/p>\n\n\n\n<p><strong>AMD: MI400 (CDNA 4) in 2027<\/strong> AMD is not resting on its laurels either. The MI400 is built on the CDNA 4 architecture and aims at approximately doubling the performance of the MI325X by 2027. AMD will ensure that it competes on top of the market.<\/p>\n\n\n\n<p>Both vendors have active roadmaps in case teams are purchasing today. Neither is a dead end.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In 2026, there is no single king of AI inference. The right GPU depends completely on your specific workload.<\/p>\n\n\n\n<p>NVIDIA B200 remains the throughput champion for short-context, low-latency, and training-heavy tasks. On the other hand, AMD MI325X shines as the memory and value champion \u2014 especially for long-context inference, single-node large model hosting, and cost-sensitive scaling.<\/p>\n\n\n\n<p>The smartest AI companies today are not locked into one vendor. They use a hybrid approach: B200 where speed and low latency matter most, and MI325X where memory capacity and better tokens-per-dollar are critical.<\/p>\n\n\n\n<p>The smartest AI companies today are not locked into one vendor. They use a hybrid approach: B200 where speed and low latency matter most, and MI325X where memory capacity and better tokens-per-dollar are critical.<\/p>\n\n\n\n<p>This kind of flexibility helps teams optimize both performance and budget at the same time. Services like <a href=\"https:\/\/www.hostrunway.com\/\" title=\"\">Hostrunway<\/a> make this easier by offering bare-metal access to both NVIDIA B200 and AMD MI325X hardware on flexible terms.<\/p>\n\n\n\n<p>This kind of flexibility helps teams optimize both performance and budget at the same time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:22px\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q1_Which_GPU_offers_the_most_competitive_cost-per-token_for_high-density_inference_workloads_in_2026\"><\/span><strong>Q1: Which GPU offers the most competitive cost-per-token for high-density inference workloads in 2026?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The AMD MI325X is also significantly cheaper-per-token than the NVIDIA B200 at independent benchmarks,particularly at high-density inference workloads, and at higher-context workloads. Its reduced cost of purchase and 256GB memory cost less per token served in hardware.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q2_Does_the_256GB_HBM3e_memory_on_the_AMD_MI325X_allow_larger_models_to_be_deployed_on_single-node_configurations_compared_to_the_NVIDIA_B200\"><\/span><strong>Q2: Does the 256GB HBM3e memory on the AMD MI325X allow larger models to be deployed on single-node configurations compared to the NVIDIA B200?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Yes. The 256GB of the MI325X enables users to model bigger models (100B+ parameters) on a single graphics card. The 192GB in the B200 also frequently uses two-GPUs on the same models, which means extra expense and complexity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q3_How_does_the_8_TBs_memory_bandwidth_of_the_NVIDIA_Blackwell_B200_impact_performance_for_latency-critical_real-time_AI_applications\"><\/span><strong>Q3: How does the 8 TB\/s memory bandwidth of the NVIDIA Blackwell B200 impact performance for latency-critical, real-time AI applications?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The bandwidth of the B200 is 8 TB\/s; hence, the data is transferred between the memory and the compute cores of the GPU more quickly. In applications with a small context that require real-time performance and Time to First Token (TTFT) is important, this speed benefit is converted into lower latency and a more enjoyable user experience.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q4_For_RAG_pipelines_requiring_massive_KV_caches_does_the_AMD_MI325X_provide_a_significant_throughput_advantage_over_NVIDIA_counterparts\"><\/span><strong>Q4: For RAG pipelines requiring massive KV caches, does the AMD MI325X provide a significant throughput advantage over NVIDIA counterparts?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Yes. RAG pipelines require huge KV caches to store context that is retrieved. The 256GB of the MI325X supports bigger caches, and the data will not have to be offloaded. This provides AMD with a throughput benefit with respect to RAG and long-document summarization workloads.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q5_Is_ROCm_mature_enough_in_2026_to_replace_CUDA_for_production_LLM_inference\"><\/span><strong>Q5: Is ROCm mature enough in 2026 to replace CUDA for production LLM inference?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In the majority of the inferences application scenarios, ROCm 6.x now runs both PyTorch and JAX and vLLM at production levels. Teams based on Triton-based kernels perform almost identically on AMD and NVIDIA. CUDA continues to lead in training and proprietary NVIDIA tools; the difference between inferences is significantly smaller than it was two years ago.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"font-size:19px\"><span class=\"ez-toc-section\" id=\"Q6_Should_a_startup_choose_the_MI325X_or_the_B200_for_its_first_GPU_cluster\"><\/span><strong>Q6: Should a startup choose the MI325X or the B200 for its first GPU cluster?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The MI325X is more robust as a starting point to startups who care more about cost-per-token and flexibility. Hardware cost is lower, large models have better memory options, and it is not required to make a commitment at the start of the budget due to providers such as Hostrunway.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Great Inference Pivot of 2026 The AI world has shifted. Training huge models was the topic of conversation a couple of years ago. Running them is the real money&hellip;<\/p>\n","protected":false},"author":1,"featured_media":1046,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[102,1],"tags":[979,980,981,977,978,982],"class_list":["post-1045","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gpu-server","category-servers","tag-amd-vs-nvidia-for-llm-inference-2026","tag-b200-vs-mi325x-inference","tag-best-ai-inference-gpu-2026-mi325x","tag-nvidia-b200-vs-amd-mi325x","tag-nvidia-b200-vs-amd-mi325x-benchmarks","tag-nvidia-b200-vs-amd-mi325x-memory-comparison"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/posts\/1045","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/comments?post=1045"}],"version-history":[{"count":1,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/posts\/1045\/revisions"}],"predecessor-version":[{"id":1047,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/posts\/1045\/revisions\/1047"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/media\/1046"}],"wp:attachment":[{"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/media?parent=1045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/categories?post=1045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hostrunway.com\/blog\/wp-json\/wp\/v2\/tags?post=1045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}